




"""simple docstring"""




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase				:     List[str]					=				logging.get_logger()






@dataclass
class        __magic_name__				:






				'''simple docstring'''
				__UpperCamelCase         =		42
				__UpperCamelCase         =		field(default_factory=UpperCAmelCase__							)
				__UpperCamelCase         =		field(default_factory=UpperCAmelCase__							)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   len(list(m.modules()					)					) == 1 or isinstance(_a						,					nn.Convad					) or isinstance(_a						,					nn.BatchNormad					)
							if has_not_submodules:
										self.traced.append(_a					)





				def __call__(			self						,					_a					):





							"""simple docstring"""
							for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook					)					)
							self.module(_a					)
							[x.remove() for x in self.handles]
							return self





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							# check the len of the state_dict keys to see if we have learnable params
							return list(filter(lambda _a					: len(list(x.state_dict().keys()					)					) > 0						,					self.traced					)					)






@dataclass
class        __magic_name__				:






				'''simple docstring'''
				__UpperCamelCase         =		42
				__UpperCamelCase         =		42
				__UpperCamelCase         =		1
				__UpperCamelCase         =		field(default_factory=UpperCAmelCase__							)
				__UpperCamelCase         =		field(default_factory=UpperCAmelCase__							)
				__UpperCamelCase         =		True





				def __call__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   Tracker(self.dest					)(_a					).parametrized
							lowerCamelCase      =   Tracker(self.src					)(_a					).parametrized

							lowerCamelCase      =   list(filter(lambda _a					: type(_a					) not in self.src_skip						,					_a					)					)
							lowerCamelCase      =   list(filter(lambda _a					: type(_a					) not in self.dest_skip						,					_a					)					)

							if len(_a					) != len(_a					) and self.raise_if_mismatch:
										raise Exception(
										    f'Numbers of operations are different. Source module has {len(_a					)} operations while'
										    f' destination module has {len(_a					)}.'					)

							for dest_m, src_m in zip(_a						,					_a					):
										dest_m.load_state_dict(src_m.state_dict()					)
										if self.verbose == 1:
													print(f'Transfered from={src_m} to={dest_m}'					)






class        __magic_name__				(			nn.Module							):






				'''simple docstring'''





				def __init__(			self						,					_a					):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase      =   []
							# - get the stem
							feature_blocks.append(("""conv1""", model.stem)					)
							# - get all the feature blocks
							for k, v in model.trunk_output.named_children():
										assert k.startswith("""block"""					), f'Unexpected layer name {k}'
										lowerCamelCase      =   len(_a					) + 1
										feature_blocks.append((f'res{block_index}', v)					)

							lowerCamelCase      =   nn.ModuleDict(_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return get_trunk_forward_outputs(
							    _a						,					out_feat_keys=_a						,					feature_blocks=self._feature_blocks						,					)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   x.split("""-"""					)
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)





				def __getitem__(			self						,					_a					):





							"""simple docstring"""
							# default to timm!
							if x not in self:
										lowerCamelCase      =   self.convert_name_to_timm(_a					)
										lowerCamelCase      =   partial(lambda: (timm.create_model(_a						,					pretrained=_a					).eval(), None)					)

							else:
										lowerCamelCase      =   super().__getitem__(_a					)

							return val






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __getitem__(			self						,					_a					):





							"""simple docstring"""
							if "seer" in x and "in1k" not in x:
										lowerCamelCase      =   RegNetModel
							else:
										lowerCamelCase      =   RegNetForImageClassification
							return val




def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   Union[str, Any]:
			for from_key, to_key in keys:
						lowerCamelCase      =   from_state_dict[from_key].clone()
						print(F'Copied key={from_key} to={to_key}'							)
			return to_state_dict



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__ = True			,					)		->   Optional[Any]:
			print(F'Converting {name}...'							)
			with torch.no_grad():
						lowerCamelCase ,  lowerCamelCase      =   from_model_func()
						lowerCamelCase      =   our_model_func(snake_case__							).eval()
						lowerCamelCase      =   ModuleTransfer(src=snake_case__			,					dest=snake_case__			,					raise_if_mismatch=snake_case__							)
						lowerCamelCase      =   torch.randn((1, 3, 2_24, 2_24)							)
						module_transfer(snake_case__							)

			if from_state_dict is not None:
						lowerCamelCase      =   []
						# for seer - in1k finetuned we have to manually copy the head
						if "seer" in name and "in1k" in name:
									lowerCamelCase      =   [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
						lowerCamelCase      =   manually_copy_vissl_head(snake_case__			,					our_model.state_dict()			,					snake_case__							)
						our_model.load_state_dict(snake_case__							)

			lowerCamelCase      =   our_model(snake_case__			,					output_hidden_states=snake_case__							)
			lowerCamelCase      =   (
			    our_outputs.logits if isinstance(snake_case__			,					snake_case__							) else our_outputs.last_hidden_state
			)

			lowerCamelCase      =   from_model(snake_case__							)
			lowerCamelCase      =   from_output[-1] if type(snake_case__							) is list else from_output

			# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
			if "seer" in name and "in1k" in name:
						lowerCamelCase      =   our_outputs.hidden_states[-1]

			assert torch.allclose(snake_case__			,					snake_case__							), "The model logits don't match the original one."

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / name			,					commit_message="""Add model"""			,					use_temp_dir=snake_case__			,					)

						lowerCamelCase      =   2_24 if """seer""" not in name else 3_84
						# we can use the convnext one
						lowerCamelCase      =   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""			,					size=snake_case__							)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / name			,					commit_message="""Add image processor"""			,					use_temp_dir=snake_case__			,					)

						print(F'Pushed {name}'							)



def        a__					(							snake_case__			,					snake_case__ = None			,					snake_case__ = True							)		->   Optional[int]:
			lowerCamelCase      =   """imagenet-1k-id2label.json"""
			lowerCamelCase      =   10_00
			lowerCamelCase      =   (1, num_labels)

			lowerCamelCase      =   """huggingface/label-files"""
			lowerCamelCase      =   num_labels
			lowerCamelCase      =   json.load(open(cached_download(hf_hub_url(snake_case__			,					snake_case__			,					repo_type="""dataset"""							)							)			,					"""r"""							)							)
			lowerCamelCase      =   {int(snake_case__							): v for k, v in idalabel.items()}

			lowerCamelCase      =   idalabel
			lowerCamelCase      =   {v: k for k, v in idalabel.items()}

			lowerCamelCase      =   partial(snake_case__			,					num_labels=snake_case__			,					idalabel=snake_case__			,					labelaid=snake_case__							)

			lowerCamelCase      =   {
			    """regnet-x-002""": ImageNetPreTrainedConfig(
			        depths=[1, 1, 4, 7]			,					hidden_sizes=[24, 56, 1_52, 3_68]			,					groups_width=8			,					layer_type="""x"""							),
			    """regnet-x-004""": ImageNetPreTrainedConfig(
			        depths=[1, 2, 7, 12]			,					hidden_sizes=[32, 64, 1_60, 3_84]			,					groups_width=16			,					layer_type="""x"""							),
			    """regnet-x-006""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 5, 7]			,					hidden_sizes=[48, 96, 2_40, 5_28]			,					groups_width=24			,					layer_type="""x"""							),
			    """regnet-x-008""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 5]			,					hidden_sizes=[64, 1_28, 2_88, 6_72]			,					groups_width=16			,					layer_type="""x"""							),
			    """regnet-x-016""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 2]			,					hidden_sizes=[72, 1_68, 4_08, 9_12]			,					groups_width=24			,					layer_type="""x"""							),
			    """regnet-x-032""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 15, 2]			,					hidden_sizes=[96, 1_92, 4_32, 10_08]			,					groups_width=48			,					layer_type="""x"""							),
			    """regnet-x-040""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 14, 2]			,					hidden_sizes=[80, 2_40, 5_60, 13_60]			,					groups_width=40			,					layer_type="""x"""							),
			    """regnet-x-064""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]			,					hidden_sizes=[1_68, 3_92, 7_84, 16_24]			,					groups_width=56			,					layer_type="""x"""							),
			    """regnet-x-080""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 15, 1]			,					hidden_sizes=[80, 2_40, 7_20, 19_20]			,					groups_width=1_20			,					layer_type="""x"""							),
			    """regnet-x-120""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]			,					hidden_sizes=[2_24, 4_48, 8_96, 22_40]			,					groups_width=1_12			,					layer_type="""x"""							),
			    """regnet-x-160""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 13, 1]			,					hidden_sizes=[2_56, 5_12, 8_96, 20_48]			,					groups_width=1_28			,					layer_type="""x"""							),
			    """regnet-x-320""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 13, 1]			,					hidden_sizes=[3_36, 6_72, 13_44, 25_20]			,					groups_width=1_68			,					layer_type="""x"""							),
			    # y variant
			    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,					hidden_sizes=[24, 56, 1_52, 3_68]			,					groups_width=8							),
			    """regnet-y-004""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 6, 6]			,					hidden_sizes=[48, 1_04, 2_08, 4_40]			,					groups_width=8							),
			    """regnet-y-006""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 4]			,					hidden_sizes=[48, 1_12, 2_56, 6_08]			,					groups_width=16							),
			    """regnet-y-008""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 8, 2]			,					hidden_sizes=[64, 1_28, 3_20, 7_68]			,					groups_width=16							),
			    """regnet-y-016""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 17, 2]			,					hidden_sizes=[48, 1_20, 3_36, 8_88]			,					groups_width=24							),
			    """regnet-y-032""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 13, 1]			,					hidden_sizes=[72, 2_16, 5_76, 15_12]			,					groups_width=24							),
			    """regnet-y-040""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 12, 2]			,					hidden_sizes=[1_28, 1_92, 5_12, 10_88]			,					groups_width=64							),
			    """regnet-y-064""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 14, 2]			,					hidden_sizes=[1_44, 2_88, 5_76, 12_96]			,					groups_width=72							),
			    """regnet-y-080""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]			,					hidden_sizes=[1_68, 4_48, 8_96, 20_16]			,					groups_width=56							),
			    """regnet-y-120""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]			,					hidden_sizes=[2_24, 4_48, 8_96, 22_40]			,					groups_width=1_12							),
			    """regnet-y-160""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 11, 1]			,					hidden_sizes=[2_24, 4_48, 12_32, 30_24]			,					groups_width=1_12							),
			    """regnet-y-320""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]			,					hidden_sizes=[2_32, 6_96, 13_92, 37_12]			,					groups_width=2_32							),
			    # models created by SEER -> https://arxiv.org/abs/2202.08360
			    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]			,					hidden_sizes=[2_32, 6_96, 13_92, 37_12]			,					groups_width=2_32							),
			    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]			,					hidden_sizes=[3_28, 9_84, 19_68, 49_20]			,					groups_width=3_28							),
			    """regnet-y-1280-seer""": RegNetConfig(
			        depths=[2, 7, 17, 1]			,					hidden_sizes=[5_28, 10_56, 29_04, 73_92]			,					groups_width=2_64							),
			    """regnet-y-2560-seer""": RegNetConfig(
			        depths=[3, 7, 16, 1]			,					hidden_sizes=[6_40, 16_96, 25_44, 50_88]			,					groups_width=6_40							),
			    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]			,					hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]			,					groups_width=10_10							),
			    # finetuned on imagenet
			    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]			,					hidden_sizes=[2_32, 6_96, 13_92, 37_12]			,					groups_width=2_32							),
			    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]			,					hidden_sizes=[3_28, 9_84, 19_68, 49_20]			,					groups_width=3_28							),
			    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]			,					hidden_sizes=[5_28, 10_56, 29_04, 73_92]			,					groups_width=2_64							),
			    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[3, 7, 16, 1]			,					hidden_sizes=[6_40, 16_96, 25_44, 50_88]			,					groups_width=6_40							),
			    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]			,					hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]			,					groups_width=10_10							),
			}

			lowerCamelCase      =   NameToOurModelFuncMap()
			lowerCamelCase      =   NameToFromModelFuncMap()
			# add seer weights logic

			def load_using_classy_vision(snake_case__			,					snake_case__							) -> Tuple[nn.Module, Dict]:
						lowerCamelCase      =   torch.hub.load_state_dict_from_url(snake_case__			,					model_dir=str(snake_case__							)			,					map_location="""cpu"""							)
						lowerCamelCase      =   model_func()
						# check if we have a head, if yes add it
						lowerCamelCase      =   files["""classy_state_dict"""]["""base_model"""]["""model"""]
						lowerCamelCase      =   model_state_dict["""trunk"""]
						model.load_state_dict(snake_case__							)
						return model.eval(), model_state_dict["heads"]

			# pretrained
			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""			,					lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27			,					group_width=10_10			,					w_a=17_44			,					w_a=620.83			,					w_m=2.52							)							)							)			,					)

			# IN1K finetuned
			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""			,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)			,					)

			lowerCamelCase      =   partial(
			    snake_case__			,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""			,					lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27			,					group_width=10_10			,					w_a=17_44			,					w_a=620.83			,					w_m=2.52							)							)							)			,					)

			if model_name:
						convert_weight_and_push(
						    snake_case__			,					names_to_from_model_map[model_name]			,					names_to_ours_model_map[model_name]			,					names_to_config[model_name]			,					snake_case__			,					snake_case__			,					)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    snake_case__			,					names_to_from_model_map[model_name]			,					names_to_ours_model_map[model_name]			,					snake_case__			,					snake_case__			,					snake_case__			,					)
			return config, expected_shape


if __name__ == "__main__":
					lowerCAmelCase				:     Dict					=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
					        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					lowerCAmelCase				:     List[Any]					=				parser.parse_args()

					lowerCAmelCase				:     Path					=				args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""simple docstring"""




from collections.abc import Sequence



def        a__					(							snake_case__			,					snake_case__ = False							)		->   float:
			if not arr:
						return 0

			lowerCamelCase      =   0 if allow_empty_subarrays else float("""-inf"""							)
			lowerCamelCase      =   0.0
			for num in arr:
						lowerCamelCase      =   max(0 if allow_empty_subarrays else num			,					curr_sum + num							)
						lowerCamelCase      =   max(snake_case__			,					snake_case__							)

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase				:     Union[str, Any]					=				[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



